dc.contributor.authorZhao, Gangqiang
dc.contributor.authorYuan, Junsong
dc.identifier.citationZhao, G., & Yuan, J. (2012). Curb detection and tracking using 3D-LIDAR scanner. 19th IEEE International Conference on Image Processing (ICIP 2012), 437-440
dc.description.abstractThis paper presents a novel road curb detection method using 3D-LIDAR scanner. To detect the curbs, the ground points are separated from the pointcloud first. Then the candidate curb points are selected using three spatial cues: the elevation difference, gradient value and normal orientation. Afterwards the false curb points caused by obstacles are removed using the short-term memory technique. Next the curbs are fitted using the parabola model. Finally, the particle filter is used to smooth the curb detection result. The proposed approach was evaluated on a dataset collected by an autonomous ground vehicle driving around the Ford Research campus and downtown Dearborn. Our curb detection results are accurate and robust despite variations introduced by moving vehicles and pedestrians, static obstacles, road curvature changes, etc.en_US
dc.rights© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/ICIP.2012.6466890].
dc.titleCurb detection and tracking using 3D-LIDAR scanneren_US
dc.typeConference Paper
dc.contributor.conferenceIEEE International Conference on Image Processing (19th : 2012 : Orlando, Florida, US)en_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.versionAccepted version

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